Regression Statistics	
Coefficient of Multiple Regression	0.987886248
Coefficient of Determination (R^2)	0.97591924
Adjusted R-Squared 			0.963878859
Standard Error				2.770601065
Observations (n)			10

These statistics exist for the equation:

CSPI = 113.6883192 + .000101746 Programmers - .000215579 Applications + 6.30455E-05 Systems 

Definitions are as follows:

Coefficient of Multiple Regression:
	
	The coefficient of multiple regression is essentially the multiple-variable equivalent of Pearson's linear correlation coefficient, r. It measures how well a given multiple-variable regression equation fits a set of data. In Excel, it may be noted as "Multiple R". 

Coefficient of Determination: 

	The variable R^2 measures how well a regression equation fits a set of data. It can be expressed as "What percentage of the variation in the data can be explained by an equation?" In Excel, it may be noted as "R Square." Statisticians argue over what formula should be used to calculate R^2; the one used in this study is simply the square of the linear correlation coefficient, as this is how Excel interprets the function.

Adjusted R-Squared: 

	The Adjusted R^2 is the same as the coefficient of determination, except that it penalizes scores when a regression equation becomes more accurate by what is considered "blind chance." While this has occured here, a decrease of 1.2% is hardly cause for concern. In Excel, it may be noted as "Adjusted R Square."

Standard Error:

	The standard error of an estimate is essentially a measure of how closely data points cluster around a regression equation. It can be thought of as the standard deviation of a regression line or curve. So, on average, the CSPI equation listed above will be $2.77 off in its predictions. 